Semiconductor companies test components to ensure that the components operate properly. The test data not only determine whether the components function properly, but also may indicate deficiencies in the manufacturing process. Accordingly, many semiconductor companies may analyze the collected data from several different components to identify problems and correct them. For example, the company may gather test data for multiple chips on each wafer among several different lots. Test data may come from a variety of sources, such as parametric electrical testing, optical inspection, scanning electron microscopy, energy dispersive x-ray spectroscopy, and focused ion beam processes for defect analysis and fault isolation. This data may be analyzed to identify common deficiencies or patterns of defects or identify parts that may exhibit quality and performance issues and to identify or classify user-defined “good parts”. Steps may then be taken to correct the problems. Testing is typically performed before device packaging (at wafer level) as well as upon completion of assembly (final test).
Gathering and analyzing test data is expensive and time consuming. Automatic testers apply signals to the components and read the corresponding output signals. The output signals may be analyzed to determine whether the component is operating properly. Each tester generates a large volume of data. For example, each tester may perform 200 tests on a single component, and each of those tests may be repeated 10 times. Consequently, a test of a single component may yield 2000 results. Because each tester is testing 100 or more components an hour and several testers may be connected to the same server, an enormous amount of data must be stored. Further, to process the data, the server typically stores the test data in a database to facilitate the manipulation and analysis of the data. Storage in a conventional database, however, requires further storage capacity as well as time to organize and store the data.
Furthermore, acquiring the test data presents a complex and painstaking process. A test engineer prepares a test program to instruct the tester to generate the input signals to the component and receive the output signals. The program tends to be very complex to ensure full and proper operation of the component. Consequently, the test program for a moderately complex integrated circuit involves a large number of tests and results. Preparing the program demands extensive design and modification to arrive at a satisfactory solution, and optimization of the program, for example to remove redundant tests or otherwise minimize test time, requires additional exertion.
The analysis of the gathered data is also difficult. The volume of the data may demand significant processing power and time. As a result, the data is not usually analyzed at product run time, but is instead typically analyzed between test runs or in other batches. To alleviate some of these burdens, some companies only sample the data from the testers and discard the rest. Analyzing less than all of the data, however, ensures that the resulting analysis cannot be fully complete and accurate. As a result, sampling degrades the complete understanding of the test results.
In addition, even when the full set of test data generated by the tester is retained, the sheer volume of the test data presents difficulties in analyzing the data and extracting meaningful results. The data may contain significant information about the devices, the testing process, and the manufacturing process that may be used to improve production, reliability, and testing. In view of the amount of data, however, isolating and presenting the information to the user or another system is challenging.
Furthermore, much of the data interpretation is performed manually by engineers who review the data and make deductions about the test and manufacturing process based on their experience and familiarity with the fabrication and test process. Although manual analysis is often effective, engineers understand the fabrication and test systems differently, and are thus prone to arriving at different subjective conclusions based on the same data. Another problem arises when experienced personnel leave the company or are otherwise unavailable, for their knowledge and understanding of the fabrication and test system and the interpretation of the test data cannot be easily transferred to other personnel.